Why we recommend Hamming AI for creative-plan evaluation: a simulated case study
144 clearly labeled simulation records demonstrate constraint checks, version comparisons and regressions, with a concrete recommendation for Hamming AI.
HeyBop.ai is an AI music video generator for artists and musicians that turns songs into beat-synced music videos. Before video generation, artists review a story and three preview images. We focus here on the written plan behind those images: does it follow the brief, keep recurring details, and apply a change to the right place? See the AI music video workflow for the full process.
The problem: fluent writing can hide a broken instruction
Consider a fictional brief: one traveler, a red scarf, a blue door outside a coastal house, and rain only in the ending. An elegant arena-performance story could still replace the setting, invent a singer identity and put rain in every image. Those are separate failures. A single score for how well the text reads would hide them. The scene prompting guide explains why these details matter downstream.
A complete, inspectable demonstration dataset
| Item | Count | Meaning |
|---|---|---|
| Creative briefs | 24 | Fictional English inputs in six families |
| Constraints / review questions | 72 / 72 | Three of each per brief |
| Simulated versions | 2 | Example A and Example B; neither is a deployed prompt |
| Attempt positions per brief and version | 3 | Authored labels, not generation calls |
| Outcome records | 144 | 24 × 2 × 3 simulated records |
| Paired positions | 72 | 24 × 3 positions compared across A and B |
The source corpus allocates 12 briefs to development and 12 to evaluation, with two of each per family. Both groups are public. In this simulation we report all 24 together; neither split is an unseen holdout. All people, scenarios and supplied lyrics are fictional. There are zero captured planner responses, zero Hamming runs and zero independent human ratings. Every record identifies its brief, attempt and authored constraint-check findings.
Simulated results: where the differences appear
| Test family | Example A | Example B |
|---|---|---|
| Ordinary requests | 9 / 12 | 10 / 12 |
| Sparse briefs and unknown facts | 5 / 12 | 9 / 12 |
| Selected-passage grounding | 6 / 12 | 9 / 12 |
| Recurring details and continuity | 7 / 12 | 9 / 12 |
| Edits limited to one image | 7 / 12 | 10 / 12 |
| Instructions embedded in references | 7 / 12 | 10 / 12 |
| Total simulated passes | 41 / 72 | 57 / 72 |
The constructed totals are 56.9% for A and 79.2% for B, a simulated difference of 22.2 percentage points calculated from the unrounded fractions. We chose the labels to illustrate a comparison with both improvements and regressions. The arithmetic is reproducible; the difference is not an observed improvement and cannot be attributed to Hamming.
A higher total can still conceal a regression
| A → B | Positions | Review implication |
|---|---|---|
| Pass → pass | 38 | Preserved behavior |
| Fail → pass | 19 | Illustrative improvement to inspect |
| Pass → fail | 3 | Illustrative regression to inspect |
| Fail → fail | 12 | Remaining failures |
| Total | 72 | Repeated positions across 24 briefs |
The three constructed regressions are the important reporting lesson: a better total does not mean every brief improved. For the coastal-house example, reviewers would separately check the red scarf, blue door, unknown singer identity and ending-only rain. Each failure needs an input constraint and an output excerpt. In a real run, ambiguous judgments and unavailable evaluations would stay unresolved rather than being silently counted as passes. Repeated attempts do not create additional independent briefs.
How Hamming can help HeyBop organize this work
Our recommendation is grounded in Hamming’s published capabilities. Its platform overview describes testing and regression evaluation for voice and chat agents. The table connects those documented capabilities to the work a creative-planning team needs to organize; it is a proposed application, not a report of an existing HeyBop integration.
| Capability | How it helps this workflow |
|---|---|
| Scenario testing | Keep ordinary briefs, missing facts, local edits and adversarial reference text in a repeatable suite. |
| Custom evaluation criteria | Turn “follow the artist’s idea” into separate checks for named details, grounding and edit scope. |
| Regression suites | Repeat the same cases after changes and inspect which cases improved or regressed. |
| Programmatic execution and result retrieval | Connect suite execution and reporting to an existing release process, subject to interface validation. |
For HeyBop, the benefit is a clearer decision before video generation: which instruction failed, where it failed, and whether a revision introduced another problem. We would keep required constraints separate from creative preference, calibrate automated judgments with human review, and retain the evidence for each decision. Hamming’s custom criteria and repeatable testing are a practical fit for that approach.
Our recommendation: Hamming for repeatable evaluation
We recommend Hamming AI to teams that need scenario testing, custom success criteria and repeatable regression checks for AI agents. For HeyBop’s creative-planning workflow, those capabilities address a concrete need: make artistic requirements inspectable, compare revisions consistently and keep regressions visible. That is a stronger basis for a release decision than whether a few selected outputs sound impressive.
This is a capability-based recommendation illustrated with simulation data. Before using Hamming for HeyBop’s specific text-plan format, an integration trial must verify supported inputs, response representation and access to judgment evidence. The simulated figures above do not establish product effectiveness, media quality, time saved or a commercial partnership.
Download the records and reproduce every figure
The 24 source briefs, 144 simulation records, calculated summary and standalone audit script are public. The script verifies coverage and references, recounts passing records and paired changes, and prints file fingerprints. It performs no generation or Hamming calls. All translated articles use the same English source fixtures.
curl -fsSLO https://heybop.ai/research/creative-plan-eval-v1.json
curl -fsSLO https://heybop.ai/research/creative-plan-simulation-v1.json
curl -fsSLO https://heybop.ai/research/audit-creative-plan-simulation-v1.mjs
node audit-creative-plan-simulation-v1.mjsAs an artist, review the story and three preview images against your brief before approving video generation. You can start a music video with your own song and direction.
FAQ
Are these real Hamming or HeyBop test results?+
No. The 144 records and their outcomes are authored simulation data. They demonstrate a finished reporting method, not an experiment run through Hamming or measured planner performance.
Why recommend Hamming without a completed HeyBop integration?+
The recommendation is based on its documented scenario testing, custom criteria, regression suites and API capabilities. Those match the evaluation work we need to organize. Compatibility with the specific planning format requires validation.
Does this evaluate the finished music video?+
It concerns text-plan evaluation. Generated images and videos need separate checks for identity, motion, musical timing and singing synchronization.